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2013 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Industrial Image Processing

Visual Quality Control in Manufacturing

verfasst von: Christian Demant, Bernd Streicher-Abel, Carsten Garnica

Verlag: Springer Berlin Heidelberg

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This practical introduction focuses on how to design integrated solutions for industrial vision tasks from individual algorithms. The book is now available in a revised second edition that takes into account the current technological developments, including camera technology and color imaging processing. It gives a hands-on guide for setting up automated visual inspection systems using real-world examples and the NeuroCheck® standard software that has proven industrial strength integrated in thousands of applications in real-world production lines. Based on many years of experience in industry, the authors explain all the essential details encountered in the creation of vision system installations. With example material and a demo version of the software found on "extras.springer.com" readers can work their way through the described inspection tasks and carry out their own experiments.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
With ever increasing demands regarding product quality and documentation, industrial vision has become a key technology. Meanwhile the use of industrial vision systems in automated manufacturing goes without saying. However, there is in many cases a lack of understanding for this modern technology. This book was written in order to remedy this condition, which was in part created by the vision industry itself. As with all areas in which PCs are increasingly used, a trend to give the user more possibilities for application development became apparent in image processing. This makes it also necessary to equip the user with adequate know-how.
Christian Demant, Carsten Garnica, Bernd Streicher-Abel
Chapter 2. Overview: Image Preprocessing
Abstract
Preprocessing algorithms frequently form the first processing step after capturing the image, as you will see in many of the examples in the following chapters. Therefore, we will start the overview chapters with an introduction to image preprocessing. Gonzalez and Woods (2008) present a comprehensive overview. To give a clear conceptual idea of the effect of the various operations we will use very simple synthetic sample images in this chapter. The application examples in the following chapters use many of these algorithms on real-world industrial images.
Christian Demant, Carsten Garnica, Bernd Streicher-Abel
Chapter 3. Positioning
Abstract
It might appear unusual to start a detailed discussion of the various application areas of industrial image processing with positioning instead of object recognition since an object must first be found before its position can be determined. However, object recognition is a rather broad term and frequently necessitates a multitude of functions to be able to assign objects to a category. Positioning, however, is structurally speaking—not necessarily algorithmically—a rather simple affair as soon as the object in question has been found. The only necessary prerequisite is the segmentation of a reference object. The main reason to begin with this topic is that it represents an absolutely essential “auxiliary science”.
Christian Demant, Carsten Garnica, Bernd Streicher-Abel
Chapter 4. Overview: Segmentation
Abstract
The concept of an object is central to the solution approach in Sect. 1.5. Indeed, it is decisive for the nature of industrial image processing since its purpose is always to gather information about objects existing in the real world represented in image scenes. In the introductory example in Sect. 1.6 and throughout Chap. 3 on positioning we have frequently used segmentation methods, i.e. algorithms which isolate objects from the scene. In these sections we have simply assumed that such methods exist and achieve the desired effects. Over time a number of such methods have been developed. The most important and most commonly used will be introduced in this chapter.
Christian Demant, Carsten Garnica, Bernd Streicher-Abel
Chapter 5. Mark Identification
Abstract
Identification of work pieces, products, and materials is indispensable in automated production processes, be it for logistics, process control or quality management. There is a variety of methods available for labeling products, which are by no means limited to visually recognizable markings. Magnetic data carriers are a simple example of a non-visual labeling method often used for process control. Non-visual methods are actually easier to identify by automated systems.
Christian Demant, Carsten Garnica, Bernd Streicher-Abel
Chapter 6. Overview: Classification
Abstract
This chapter will give a brief general introduction into the field of classification and an overview of some important types of classifiers. Classification is a research area in its own right, which over the last 40 years has combined results from disciplines as different as biology, psychology, mathematics, and computer science. Covering such an extensive and varied scientific field with even a minimal claim to completeness is naturally beyond the scope of this text, as is treating all commonly used types of classifiers in mathematical detail. Yet we will at least mention the principal types to give the reader some orientation in this important area of pattern recognition tasks. We will therefore discuss the multilayer perceptron neural network used in the pattern recognition examples of Chap. 5 in depth.
Christian Demant, Carsten Garnica, Bernd Streicher-Abel
Chapter 7. Gauging
Abstract
Dimensional or shape checking and gauging is one of the most demanding applications of industrial image processing, algorithmically as well as with regard to systems engineering and facility construction. It is actually possible to reach accuracies of just a few light wavelengths, but this requires considerable effort. As in every technical discipline, precise results cannot be achieved without corresponding diligence, especially with regard to peripherals, selection of components, mechanical setup, illumination, and image capture. Quality lost in the sensory chain is lost forever. For this reason, an overview chapter on illumination and image capturing will immediately follow this chapter.
Christian Demant, Carsten Garnica, Bernd Streicher-Abel
Chapter 8. Overview: Image Acquisition and Illumination
Abstract
In the previous chapters we have encountered a number of ways to process digital images, to recognize objects therein and to evaluate them. So far, however, we have not explained how the digital image is acquired whose properties and quality are of the utmost importance in solving an image processing task.
Christian Demant, Carsten Garnica, Bernd Streicher-Abel
Chapter 9. Presence Verification
Abstract
Viewed from the perspective of the problem to be solved, presence verification appears easily grasped and structurally rather simple. This is the reason why presence verification is often used as an introductory example to image processing. In reality, the concept of presence verification is quite difficult to define precisely: is it simply counting objects or are properties of these objects of importance? Are these properties simple features or is the entire appearance of the object to be considered? Do properties relate to individual objects only or do we have to take relationships between objects into account, for example, when doing assembly verification.
Christian Demant, Carsten Garnica, Bernd Streicher-Abel
Chapter 10. Overview: Object Features
Abstract
Most examples in the preceding chapters used various features to check image objects for validity. It is therefore high time to give an overview of such features. From the numerous features described in literature, we have selected some that have proved their worth in many industrial applications. At the same time, we will present some further insights into the difficulties encountered when applying common everyday notions to the discrete world of digital images.
Christian Demant, Carsten Garnica, Bernd Streicher-Abel
Chapter 11. Color Image Processing
Abstract
This chapter will present an introduction to color image processing. We define color image processing as the evaluation of color information in images for industrial image processing applications. Even though color television has been the standard for decades and the consumer market offers almost exclusively color cameras, most inspection lines in machine vision are still equipped with gray level cameras, and inspection tasks are solved evaluating only the brightness information. This is even more remarkable considering that color is an essential part of the visual information perceived by humans. According to Russ (2007), a human can distinguish merely 20–30 gray levels but about 1,000 colors and shades of colors; this illustrates how important color information is for us.
Christian Demant, Carsten Garnica, Bernd Streicher-Abel
Chapter 12. Implementation of Industrial Image Processing Applications
Abstract
In the preceding chapters we have described a variety of methods and algorithms from the field of image processing and shown their application in various industrial projects. In conclusion, we would like to turn to some aspects that are important for the implementation of industrial image processing applications.
Christian Demant, Carsten Garnica, Bernd Streicher-Abel
Backmatter
Metadaten
Titel
Industrial Image Processing
verfasst von
Christian Demant
Bernd Streicher-Abel
Carsten Garnica
Copyright-Jahr
2013
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-642-33905-9
Print ISBN
978-3-642-33904-2
DOI
https://doi.org/10.1007/978-3-642-33905-9